All Questions
Tagged with train-test-split predictive-models
8 questions
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Using a model to evaluate over or under-priced rental prices for the same apartments used in training
If I have a machine learning model which predicts the rental prices of apartments, can I use the model once complete to analyse the prediction for the same apartments I used to train the model so I ...
- 83
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Lasso regression test MSE lower than train MSE
Im currently using Lasso to build a predictive model for numeric variable .
Before scaling the features I split the data for train test and validation . I have a feature named 'year' and i wanted the ...
1
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0
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316
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random split vs temporal/time based split
Some background: I want to train a regression model to predict future prices for used cars. I have about 85,000 observations collected from November 2022 to June 2023 and have around 80 different ...
- 267
-1
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Should I use transformer.fit_transform(X_test, y_test) or not?
tl-dr: The function model.fit() is different from transformer.fit(). My idea is to make all transformations needed on the training set and after that on the test set with fit_transform in both.
Hi!
I'...
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Running model on full dataset or just test set?
I'm new to using XGBoost and I'm confused about how we should obtain the XGBoost predicted values for each data point. For example, the process for fitting and evaluating an XGBoost model is:
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- 65
2
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1
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847
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How to distinguish two versions of R-squared calculated on test set?
I've come across two ways that people calculate R-squared on a test set:
Calculate the square of the correlation between predictions and actual values (in practice, I've seen people do this in R by ...
- 4,404
4
votes
1
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642
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Rule based label - random split vs time-based split
We have a dataset of 977 records (77:23 class ratio) where we try to predict a binary outcome using random forests and neural networks. whether supplier met the target or not. However, we didn't have ...
- 3,342
2
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random split vs time based split of train and test data
I have been working on binary classification problem using algorithms such as Random Forest, Boosting methods, neural networks and logistic regression. I have data from Jan 2017 to Jan 2022. We wish ...
- 3,342